from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as k 

batch_size = 128
num_classes = 10
epochs = 12

img_rows, img_cols = 28, 28
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if k.image_data_format() == 'channels_first':
	x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
	x_test = x_test.reshape(x_test[0], 1, img_rows, img_cols)
	input_shape = (1, img_rows, img_cols)
else:
	x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
	x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
	input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape: ', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape))
model.add(Conv2D(64, kernel_size = (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))

model.compile(loss = keras.losses.categorical_crossentropy, optimizer = keras.optimizers.Adadelta(), metrics = ['accuracy'])
model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 8, validation_data = (x_test, y_test))
score = model.evaluate(x_test, y_test, verbose = 0)
print('Test loss', score[0])
print('Test accuracy', score[1])